Step-by-Step Guide to Reading Parquet Files in Spark

Here is an overview of how Spark reads a Parquet file and shares it across the Spark cluster for better performance. Spark reads the Parquet file and uses its architecture to spread the data across the cluster, allowing for parallel processing.

Step-by-Step Process of Reading and Distributing a Parquet File in Spark

• Reading the file

• Logical plan

• Data partitioning

• Physical plan

• Task assignment

• Transformation

• Completion

By the same token, here is a step-by-step process of reading and distributing of parquet file.

Reading the Parquet File

DataFrame API Call: The process typically begins with a DataFrame API call in Spark, such as spark.read.parquet("file_path"). This command instructs Spark to read the specified Parquet file(s) from a file system (e.g., HDFS, S3, local storage).

Parquet Metadata Handling: Parquet files store metadata, such as file schema, column statistics, row groups, and column data offsets. When Spark reads a Parquet file, it first reads the metadata to understand the structure and size of the data.

Logical Plan Generation

Logical Plan Creation: Spark’s Catalyst optimizer generates a logical plan for the query or transformation involving the Parquet data. This logical plan is based on the DataFrame operations. These operations include filters, joins, or aggregations. It is a high-level representation of the required data transformations.

Data Partitioning

Block Splitting: The Parquet file is divided into smaller blocks or partitions. Each block corresponds to a set of rows. These are often defined by the Parquet format’s row groups. Each block is the smallest unit of data that Spark processes in parallel.

Partition Information Retrieval: Spark retrieves metadata from the Parquet file’s footer. This determines how many partitions (blocks) are present. It also identifies the location of each block within the file.

Physical Plan Optimization

Catalyst Optimizer and Cost-Based Optimization (CBO): Spark’s Catalyst Optimizer further refines the logical plan to generate a physical plan. It uses rules and cost-based optimization techniques. These techniques decide how to execute the operations efficiently. They consider data locality and the cost of moving data across the cluster.

Task Assignment

Parallel Task Assignment: Spark breaks the work into multiple tasks, each responsible for processing a specific partition (block) of data. The number of tasks usually corresponds to the number of partitions in the Parquet file.

Scheduling Tasks on Executors: Spark’s driver program schedules these tasks on worker nodes (executors) across the cluster. The tasks are distributed based on data locality. Preferably, tasks are assigned to nodes where the data is located to minimize network transfer. The distribution also depends on the availability of resources.

Data Loading and Processing

Loading Data into Executors: Each executor reads the assigned partitions (blocks) from the Parquet file. The Parquet reader deserializes the data into an in-memory format, typically as Spark SQL’s internal columnar format, for processing.

Columnar Processing: Spark leverages the columnar nature of Parquet files to read only the required columns for a query. This reduces the amount of data read into memory and speeds up query performance.

Data Transformation and Action Execution

Execution of Transformations: Each task processes its partition(data) according to the transformations defined in the physical plan (e.g., filters, maps, joins). Spark executes these transformations in parallel across the cluster.

Data Shuffling (if required): Operations like join or groupBy need data to be redistributed across the cluster. In these cases, Spark redistributes the data. This redistribution occurs as required. This process is called “shuffling.” Spark creates a new set of partitions aligned with the requirements of the operation.

Task Completion and Result Aggregation

Task Completion: Once each task completes its execution, it returns the results to the Spark driver.

Aggregation of Results: The Spark driver aggregates the results from all tasks. This produces the final output of the query or transformation.

How Spark Distributes Parquet Data Across the Cluster

In the same fashion, here are the techniques that how cluster improves performance while distributing.

Data Locality Optimization: Spark tries to schedule tasks on executors located close to where the data is stored (data locality). This minimizes network I/O by reducing the need to move data across the network.

Parallelism via Partitioning: The data is distributed across multiple partitions, and each partition is processed in parallel by different executors. This parallel processing allows Spark to handle large datasets efficiently by leveraging the full computational power of the cluster.

Predicate Pushdown and Column Pruning: Spark uses metadata from the Parquet file to perform predicate pushdown. Predicate pushdown filters data at the source. Spark also performs column pruning. Column pruning means only reading necessary columns. This reduces the amount of data read and processed, improving performance.

Fault Tolerance and Task Resilience: If a task fails, Spark can reassign it to another executor. The use of partitions ensures that only the failed partition needs to be reprocessed, maintaining fault tolerance and resilience.

Summary

To sum up, when Spark reads a Parquet file, it:

1. Reads metadata to understand the file structure.
2. Creates a logical plan for the query.
3. Divide the file into partitions (blocks) for parallel processing.
4. Optimizes the plan with the Catalyst optimizer.
5. Assigns tasks to executors across the cluster.
6. Reads and processes data in parallel, leveraging data locality, columnar processing, and predicate pushdown.

By distributing the data efficiently, Spark ensures high-performance processing of large Parquet datasets.